Overview

Dataset statistics

Number of variables14
Number of observations158424
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.9 MiB
Average record size in memory112.0 B

Variable types

Numeric12
Categorical2

Alerts

Date has a high cardinality: 861 distinct values High cardinality
Country has a high cardinality: 184 distinct values High cardinality
Unnamed: 0 is highly correlated with Confirmed_Cases and 5 other fieldsHigh correlation
Confirmed_Cases is highly correlated with Unnamed: 0 and 8 other fieldsHigh correlation
Deaths is highly correlated with Unnamed: 0 and 9 other fieldsHigh correlation
New_Cases is highly correlated with Confirmed_Cases and 5 other fieldsHigh correlation
New_Deaths is highly correlated with Confirmed_Cases and 4 other fieldsHigh correlation
Doses_admin is highly correlated with Unnamed: 0 and 5 other fieldsHigh correlation
GDP is highly correlated with Confirmed_Cases and 5 other fieldsHigh correlation
Population is highly correlated with Deaths and 1 other fieldsHigh correlation
Confirmed_Cases_rel is highly correlated with Unnamed: 0 and 6 other fieldsHigh correlation
Deaths_rel is highly correlated with Unnamed: 0 and 7 other fieldsHigh correlation
Doses_admin_per_100 is highly correlated with Unnamed: 0 and 5 other fieldsHigh correlation
GDP_pro_Kopf is highly correlated with GDPHigh correlation
Unnamed: 0 is highly correlated with Confirmed_Cases_rel and 1 other fieldsHigh correlation
Confirmed_Cases is highly correlated with Deaths and 2 other fieldsHigh correlation
Deaths is highly correlated with Confirmed_Cases and 2 other fieldsHigh correlation
New_Cases is highly correlated with Confirmed_Cases and 1 other fieldsHigh correlation
New_Deaths is highly correlated with Deaths and 1 other fieldsHigh correlation
Doses_admin is highly correlated with PopulationHigh correlation
GDP is highly correlated with Confirmed_Cases and 2 other fieldsHigh correlation
Population is highly correlated with Doses_admin and 1 other fieldsHigh correlation
Confirmed_Cases_rel is highly correlated with Unnamed: 0 and 2 other fieldsHigh correlation
Deaths_rel is highly correlated with Confirmed_Cases_rel and 1 other fieldsHigh correlation
Doses_admin_per_100 is highly correlated with Unnamed: 0 and 2 other fieldsHigh correlation
Unnamed: 0 is highly correlated with Doses_admin and 2 other fieldsHigh correlation
Confirmed_Cases is highly correlated with Deaths and 5 other fieldsHigh correlation
Deaths is highly correlated with Confirmed_Cases and 5 other fieldsHigh correlation
New_Cases is highly correlated with Confirmed_Cases and 2 other fieldsHigh correlation
New_Deaths is highly correlated with Confirmed_Cases and 2 other fieldsHigh correlation
Doses_admin is highly correlated with Unnamed: 0 and 3 other fieldsHigh correlation
GDP is highly correlated with PopulationHigh correlation
Population is highly correlated with GDPHigh correlation
Confirmed_Cases_rel is highly correlated with Unnamed: 0 and 4 other fieldsHigh correlation
Deaths_rel is highly correlated with Confirmed_Cases and 2 other fieldsHigh correlation
Doses_admin_per_100 is highly correlated with Unnamed: 0 and 2 other fieldsHigh correlation
Unnamed: 0 is highly correlated with Confirmed_Cases_rel and 1 other fieldsHigh correlation
Confirmed_Cases is highly correlated with Deaths and 4 other fieldsHigh correlation
Deaths is highly correlated with Confirmed_Cases and 5 other fieldsHigh correlation
New_Cases is highly correlated with Confirmed_Cases and 1 other fieldsHigh correlation
New_Deaths is highly correlated with PopulationHigh correlation
Doses_admin is highly correlated with Confirmed_Cases and 3 other fieldsHigh correlation
GDP is highly correlated with Confirmed_Cases and 4 other fieldsHigh correlation
Population is highly correlated with Confirmed_Cases and 4 other fieldsHigh correlation
Confirmed_Cases_rel is highly correlated with Unnamed: 0 and 2 other fieldsHigh correlation
Deaths_rel is highly correlated with Deaths and 2 other fieldsHigh correlation
Doses_admin_per_100 is highly correlated with Unnamed: 0 and 2 other fieldsHigh correlation
GDP_pro_Kopf is highly correlated with GDPHigh correlation
New_Cases is highly skewed (γ1 = 20.74309917) Skewed
Unnamed: 0 is uniformly distributed Uniform
Date is uniformly distributed Uniform
Country is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
Confirmed_Cases has 10680 (6.7%) zeros Zeros
Deaths has 21751 (13.7%) zeros Zeros
New_Cases has 44052 (27.8%) zeros Zeros
New_Deaths has 78867 (49.8%) zeros Zeros
Doses_admin has 79868 (50.4%) zeros Zeros
GDP has 6027 (3.8%) zeros Zeros
Confirmed_Cases_rel has 10680 (6.7%) zeros Zeros
Deaths_rel has 21751 (13.7%) zeros Zeros
Doses_admin_per_100 has 79868 (50.4%) zeros Zeros
GDP_pro_Kopf has 6027 (3.8%) zeros Zeros

Reproduction

Analysis started2022-06-02 09:15:07.352544
Analysis finished2022-06-02 09:15:53.785911
Duration46.43 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct158424
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84808.55435
Minimum0
Maximum169616
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-06-02T11:15:54.006878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8481.15
Q142406.75
median84810.5
Q3127211.25
95-th percentile161133.85
Maximum169616
Range169616
Interquartile range (IQR)84804.5

Descriptive statistics

Standard deviation48964.36403
Coefficient of variation (CV)0.5773517119
Kurtosis-1.200000079
Mean84808.55435
Median Absolute Deviation (MAD)42402.5
Skewness-6.325427191 × 10-11
Sum1.343571041 × 1010
Variance2397508945
MonotonicityStrictly increasing
2022-06-02T11:15:54.180017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
1130801
 
< 0.1%
1130711
 
< 0.1%
1130721
 
< 0.1%
1130751
 
< 0.1%
1130761
 
< 0.1%
1130771
 
< 0.1%
1130781
 
< 0.1%
1130791
 
< 0.1%
1130811
 
< 0.1%
Other values (158414)158414
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
1696161
< 0.1%
1696151
< 0.1%
1696141
< 0.1%
1696111
< 0.1%
1696101
< 0.1%
1696091
< 0.1%
1696081
< 0.1%
1696071
< 0.1%
1696061
< 0.1%
1696051
< 0.1%

Date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct861
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2020-01-22
 
184
2021-08-10
 
184
2021-08-12
 
184
2021-08-13
 
184
2021-08-14
 
184
Other values (856)
157504 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1584240
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-01-22
2nd row2020-01-22
3rd row2020-01-22
4th row2020-01-22
5th row2020-01-22

Common Values

ValueCountFrequency (%)
2020-01-22184
 
0.1%
2021-08-10184
 
0.1%
2021-08-12184
 
0.1%
2021-08-13184
 
0.1%
2021-08-14184
 
0.1%
2021-08-15184
 
0.1%
2021-08-16184
 
0.1%
2021-08-17184
 
0.1%
2021-08-18184
 
0.1%
2021-08-19184
 
0.1%
Other values (851)156584
98.8%

Length

2022-06-02T11:15:54.349288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-01-22184
 
0.1%
2020-03-15184
 
0.1%
2020-01-24184
 
0.1%
2020-01-25184
 
0.1%
2020-01-26184
 
0.1%
2020-01-27184
 
0.1%
2020-01-28184
 
0.1%
2020-01-29184
 
0.1%
2020-01-30184
 
0.1%
2020-01-31184
 
0.1%
Other values (851)156584
98.8%

Most occurring characters

ValueCountFrequency (%)
2439944
27.8%
0419336
26.5%
-316848
20.0%
1195408
12.3%
340664
 
2.6%
532752
 
2.1%
432200
 
2.0%
827048
 
1.7%
727048
 
1.7%
626680
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1267392
80.0%
Dash Punctuation316848
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2439944
34.7%
0419336
33.1%
1195408
15.4%
340664
 
3.2%
532752
 
2.6%
432200
 
2.5%
827048
 
2.1%
727048
 
2.1%
626680
 
2.1%
926312
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
-316848
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1584240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2439944
27.8%
0419336
26.5%
-316848
20.0%
1195408
12.3%
340664
 
2.6%
532752
 
2.1%
432200
 
2.0%
827048
 
1.7%
727048
 
1.7%
626680
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1584240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2439944
27.8%
0419336
26.5%
-316848
20.0%
1195408
12.3%
340664
 
2.6%
532752
 
2.1%
432200
 
2.0%
827048
 
1.7%
727048
 
1.7%
626680
 
1.7%

Country
Categorical

HIGH CARDINALITY
UNIFORM

Distinct184
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Afghanistan
 
861
Panama
 
861
New Zealand
 
861
Nicaragua
 
861
Niger
 
861
Other values (179)
154119 

Length

Max length32
Median length22
Mean length8.282608696
Min length2

Characters and Unicode

Total characters1312164
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAndorra
5th rowAngola

Common Values

ValueCountFrequency (%)
Afghanistan861
 
0.5%
Panama861
 
0.5%
New Zealand861
 
0.5%
Nicaragua861
 
0.5%
Niger861
 
0.5%
Nigeria861
 
0.5%
North Macedonia861
 
0.5%
Norway861
 
0.5%
Oman861
 
0.5%
Pakistan861
 
0.5%
Other values (174)149814
94.6%

Length

2022-06-02T11:15:54.518251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and5166
 
2.7%
guinea2583
 
1.3%
south2583
 
1.3%
saint2583
 
1.3%
united1722
 
0.9%
new1722
 
0.9%
sudan1722
 
0.9%
republic1722
 
0.9%
islands1722
 
0.9%
andorra861
 
0.4%
Other values (198)170478
88.4%

Most occurring characters

ValueCountFrequency (%)
a210084
16.0%
i121401
 
9.3%
n107625
 
8.2%
e86100
 
6.6%
r72324
 
5.5%
o68019
 
5.2%
u49938
 
3.8%
t49077
 
3.7%
l45633
 
3.5%
s42189
 
3.2%
Other values (43)459774
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1085721
82.7%
Uppercase Letter189420
 
14.4%
Space Separator34440
 
2.6%
Dash Punctuation1722
 
0.1%
Other Punctuation861
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a210084
19.3%
i121401
11.2%
n107625
9.9%
e86100
 
7.9%
r72324
 
6.7%
o68019
 
6.3%
u49938
 
4.6%
t49077
 
4.5%
l45633
 
4.2%
s42189
 
3.9%
Other values (16)233331
21.5%
Uppercase Letter
ValueCountFrequency (%)
S24969
13.2%
B16359
 
8.6%
M16359
 
8.6%
A12915
 
6.8%
G12054
 
6.4%
C12054
 
6.4%
L11193
 
5.9%
N9471
 
5.0%
T9471
 
5.0%
I8610
 
4.5%
Other values (14)55965
29.5%
Space Separator
ValueCountFrequency (%)
34440
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1722
100.0%
Other Punctuation
ValueCountFrequency (%)
,861
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1275141
97.2%
Common37023
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a210084
16.5%
i121401
 
9.5%
n107625
 
8.4%
e86100
 
6.8%
r72324
 
5.7%
o68019
 
5.3%
u49938
 
3.9%
t49077
 
3.8%
l45633
 
3.6%
s42189
 
3.3%
Other values (40)422751
33.2%
Common
ValueCountFrequency (%)
34440
93.0%
-1722
 
4.7%
,861
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1312164
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a210084
16.0%
i121401
 
9.3%
n107625
 
8.2%
e86100
 
6.6%
r72324
 
5.5%
o68019
 
5.2%
u49938
 
3.8%
t49077
 
3.7%
l45633
 
3.5%
s42189
 
3.2%
Other values (43)459774
35.0%

Confirmed_Cases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct87371
Distinct (%)55.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean894053.5083
Minimum0
Maximum84210815
Zeros10680
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-06-02T11:15:54.698453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12314
median33820.5
Q3323619.75
95-th percentile3727166.15
Maximum84210815
Range84210815
Interquartile range (IQR)321305.75

Descriptive statistics

Standard deviation4088102.023
Coefficient of variation (CV)4.572547375
Kurtosis156.4148203
Mean894053.5083
Median Absolute Deviation (MAD)33819.5
Skewness10.85911008
Sum1.41639533 × 1011
Variance1.671257815 × 1013
MonotonicityNot monotonic
2022-06-02T11:15:54.861481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010680
 
6.7%
11428
 
0.9%
4798
 
0.5%
3673
 
0.4%
2667
 
0.4%
509450
 
0.3%
18378
 
0.2%
7361
 
0.2%
20349
 
0.2%
24308
 
0.2%
Other values (87361)142332
89.8%
ValueCountFrequency (%)
010680
6.7%
11428
 
0.9%
2667
 
0.4%
3673
 
0.4%
4798
 
0.5%
5206
 
0.1%
6183
 
0.1%
7361
 
0.2%
8285
 
0.2%
9119
 
0.1%
ValueCountFrequency (%)
842108151
< 0.1%
840124121
< 0.1%
839843951
< 0.1%
839801071
< 0.1%
839690641
< 0.1%
838639791
< 0.1%
837213691
< 0.1%
835051331
< 0.1%
834253391
< 0.1%
832854021
< 0.1%

Deaths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct31347
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15990.60038
Minimum0
Maximum1007032
Zeros21751
Zeros (%)13.7%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-06-02T11:15:55.052458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q134
median572
Q35701.25
95-th percentile85218
Maximum1007032
Range1007032
Interquartile range (IQR)5667.25

Descriptive statistics

Standard deviation64474.25843
Coefficient of variation (CV)4.032009863
Kurtosis85.55148648
Mean15990.60038
Median Absolute Deviation (MAD)572
Skewness8.290827404
Sum2533294874
Variance4156930000
MonotonicityNot monotonic
2022-06-02T11:15:55.234492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
021751
 
13.7%
12487
 
1.6%
31671
 
1.1%
21318
 
0.8%
10996
 
0.6%
7955
 
0.6%
21730
 
0.5%
4653
 
0.4%
125604
 
0.4%
9602
 
0.4%
Other values (31337)126657
79.9%
ValueCountFrequency (%)
021751
13.7%
12487
 
1.6%
21318
 
0.8%
31671
 
1.1%
4653
 
0.4%
5457
 
0.3%
6571
 
0.4%
7955
 
0.6%
8505
 
0.3%
9602
 
0.4%
ValueCountFrequency (%)
10070321
< 0.1%
10066481
< 0.1%
10066211
< 0.1%
10066141
< 0.1%
10065811
< 0.1%
10060111
< 0.1%
10057061
< 0.1%
10046461
< 0.1%
10042931
< 0.1%
10040891
< 0.1%

New_Cases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct16761
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3324.085265
Minimum0
Maximum1383917
Zeros44052
Zeros (%)27.8%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-06-02T11:15:55.435490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median66
Q3841
95-th percentile13027.85
Maximum1383917
Range1383917
Interquartile range (IQR)841

Descriptive statistics

Standard deviation19525.66553
Coefficient of variation (CV)5.873996597
Kurtosis738.4188298
Mean3324.085265
Median Absolute Deviation (MAD)66
Skewness20.74309917
Sum526614884
Variance381251614.5
MonotonicityNot monotonic
2022-06-02T11:15:55.644454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
044052
27.8%
13036
 
1.9%
22156
 
1.4%
31736
 
1.1%
41512
 
1.0%
51303
 
0.8%
61254
 
0.8%
71070
 
0.7%
8962
 
0.6%
9890
 
0.6%
Other values (16751)100453
63.4%
ValueCountFrequency (%)
044052
27.8%
13036
 
1.9%
22156
 
1.4%
31736
 
1.1%
41512
 
1.0%
51303
 
0.8%
61254
 
0.8%
71070
 
0.7%
8962
 
0.6%
9890
 
0.6%
ValueCountFrequency (%)
13839171
< 0.1%
11295191
< 0.1%
10449581
< 0.1%
9221671
< 0.1%
9081341
< 0.1%
8800671
< 0.1%
8697571
< 0.1%
8613411
< 0.1%
8486341
< 0.1%
8481691
< 0.1%

New_Deaths
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1745
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.50823108
Minimum-1918
Maximum11447
Zeros78867
Zeros (%)49.8%
Negative150
Negative (%)0.1%
Memory size1.2 MiB
2022-06-02T11:15:55.838721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1918
5-th percentile0
Q10
median1
Q311
95-th percentile174
Maximum11447
Range13365
Interquartile range (IQR)11

Descriptive statistics

Standard deviation187.1955962
Coefficient of variation (CV)4.738141676
Kurtosis291.7866235
Mean39.50823108
Median Absolute Deviation (MAD)1
Skewness12.96063823
Sum6259052
Variance35042.19124
MonotonicityNot monotonic
2022-06-02T11:15:56.027824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
078867
49.8%
110426
 
6.6%
26558
 
4.1%
34897
 
3.1%
43942
 
2.5%
53224
 
2.0%
62698
 
1.7%
72244
 
1.4%
82093
 
1.3%
91855
 
1.2%
Other values (1735)41620
26.3%
ValueCountFrequency (%)
-19181
< 0.1%
-6791
< 0.1%
-4431
< 0.1%
-2871
< 0.1%
-2811
< 0.1%
-2321
< 0.1%
-2281
< 0.1%
-2171
< 0.1%
-2131
< 0.1%
-1841
< 0.1%
ValueCountFrequency (%)
114471
< 0.1%
87861
< 0.1%
73741
< 0.1%
45291
< 0.1%
44541
< 0.1%
44111
< 0.1%
43761
< 0.1%
43291
< 0.1%
42721
< 0.1%
42091
< 0.1%

Doses_admin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct36824
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18134459.34
Minimum0
Maximum3442312826
Zeros79868
Zeros (%)50.4%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-06-02T11:15:56.212311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32286778.25
95-th percentile55373993.55
Maximum3442312826
Range3442312826
Interquartile range (IQR)2286778.25

Descriptive statistics

Standard deviation143219578.8
Coefficient of variation (CV)7.897648126
Kurtosis316.5605895
Mean18134459.34
Median Absolute Deviation (MAD)0
Skewness16.68281124
Sum2.872933586 × 1012
Variance2.051184776 × 1016
MonotonicityNot monotonic
2022-06-02T11:15:56.386471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
079868
50.4%
49980199
 
0.1%
44526119
 
0.1%
1924950119
 
0.1%
6046796
 
0.1%
155387481
 
0.1%
125925081
 
0.1%
63796181
 
0.1%
238000080
 
0.1%
203689679
 
< 0.1%
Other values (36814)77621
49.0%
ValueCountFrequency (%)
079868
50.4%
11
 
< 0.1%
22
 
< 0.1%
53
 
< 0.1%
65
 
< 0.1%
116
 
< 0.1%
121
 
< 0.1%
181
 
< 0.1%
202
 
< 0.1%
261
 
< 0.1%
ValueCountFrequency (%)
34423128267
< 0.1%
34187249212
 
< 0.1%
339900330212
< 0.1%
33814784753
 
< 0.1%
33641692866
< 0.1%
33628500005
< 0.1%
33362850001
 
< 0.1%
33340180001
 
< 0.1%
33317710001
 
< 0.1%
33297660001
 
< 0.1%

GDP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct178
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.919941304 × 1011
Minimum0
Maximum2.219812 × 1013
Zeros6027
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-06-02T11:15:56.792468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile324000000
Q17431750000
median3.3135 × 1010
Q32.47677 × 1011
95-th percentile1.83249 × 1012
Maximum2.219812 × 1013
Range2.219812 × 1013
Interquartile range (IQR)2.4024525 × 1011

Descriptive statistics

Standard deviation2.079641555 × 1012
Coefficient of variation (CV)4.226964157
Kurtosis76.39857892
Mean4.919941304 × 1011
Median Absolute Deviation (MAD)3.16405 × 1010
Skewness8.346709972
Sum7.794367812 × 1016
Variance4.324908996 × 1024
MonotonicityNot monotonic
2022-06-02T11:15:56.959602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06027
 
3.8%
2.0682 × 1010861
 
0.5%
2.2114 × 1010861
 
0.5%
2.24943 × 1011861
 
0.5%
1.2459 × 1010861
 
0.5%
1.0628 × 1010861
 
0.5%
4.96122 × 1011861
 
0.5%
1.37 × 1010861
 
0.5%
4.38623 × 1011861
 
0.5%
8.4158 × 1010861
 
0.5%
Other values (168)144648
91.3%
ValueCountFrequency (%)
06027
3.8%
196000000861
 
0.5%
228000000861
 
0.5%
324000000861
 
0.5%
389000000861
 
0.5%
512000000861
 
0.5%
527000000861
 
0.5%
590000000861
 
0.5%
773000000861
 
0.5%
903000000861
 
0.5%
ValueCountFrequency (%)
2.219812 × 1013861
0.5%
1.54681 × 1013861
0.5%
5.49542 × 1012861
0.5%
4.15712 × 1012861
0.5%
3.25772 × 1012861
0.5%
2.92708 × 1012861
0.5%
2.87605 × 1012861
0.5%
2.09045 × 1012861
0.5%
2.0625 × 1012861
0.5%
1.83249 × 1012861
0.5%

Population
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct184
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41803750.43
Minimum18233
Maximum1448471400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-06-02T11:15:57.149638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum18233
5-th percentile99509
Q12080486.5
median9179439.5
Q330457903.25
95-th percentile131562772
Maximum1448471400
Range1448453167
Interquartile range (IQR)28377416.75

Descriptive statistics

Standard deviation153263243.2
Coefficient of variation (CV)3.666255817
Kurtosis69.88845384
Mean41803750.43
Median Absolute Deviation (MAD)8505650
Skewness8.123593678
Sum6.622717359 × 1012
Variance2.348962172 × 1016
MonotonicityNot monotonic
2022-06-02T11:15:57.340947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40754388861
 
0.5%
4446964861
 
0.5%
4898203861
 
0.5%
6779100861
 
0.5%
26083660861
 
0.5%
216746934861
 
0.5%
2081304861
 
0.5%
5511370861
 
0.5%
5323993861
 
0.5%
229488994861
 
0.5%
Other values (174)149814
94.6%
ValueCountFrequency (%)
18233861
0.5%
34085861
0.5%
38387861
0.5%
39783861
0.5%
53871861
0.5%
60057861
0.5%
72344861
0.5%
77463861
0.5%
99426861
0.5%
99509861
0.5%
ValueCountFrequency (%)
1448471400861
0.5%
1406631776861
0.5%
334805269861
0.5%
279134505861
0.5%
229488994861
0.5%
216746934861
0.5%
215353593861
0.5%
167885689861
0.5%
145805947861
0.5%
131562772861
0.5%

Confirmed_Cases_rel
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct114463
Distinct (%)72.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.742723817
Minimum0
Maximum55.37353317
Zeros10680
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-06-02T11:15:57.555121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.03839449935
median0.4578384953
Q34.451728152
95-th percentile16.89306146
Maximum55.37353317
Range55.37353317
Interquartile range (IQR)4.413333653

Descriptive statistics

Standard deviation7.243292081
Coefficient of variation (CV)1.935299647
Kurtosis13.55968946
Mean3.742723817
Median Absolute Deviation (MAD)0.4578384953
Skewness3.342241636
Sum592937.278
Variance52.46528016
MonotonicityNot monotonic
2022-06-02T11:15:57.748304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010680
 
6.7%
0.0008511435113459
 
0.3%
0.0008041258449441
 
0.3%
0.006660339344415
 
0.3%
0.001483393411339
 
0.2%
0.002773313513255
 
0.2%
0.001620496034243
 
0.2%
0.001242884486189
 
0.1%
0.1377258306155
 
0.1%
0.0003107211216118
 
0.1%
Other values (114453)145130
91.6%
ValueCountFrequency (%)
010680
6.7%
7.109181074 × 10-83
 
< 0.1%
1.421836215 × 10-71
 
< 0.1%
2.132754322 × 10-728
 
< 0.1%
2.98681082 × 10-72
 
< 0.1%
3.554590537 × 10-72
 
< 0.1%
4.613675412 × 10-710
 
< 0.1%
4.643525962 × 10-73
 
< 0.1%
5.97362164 × 10-72
 
< 0.1%
7.165004556 × 10-74
 
< 0.1%
ValueCountFrequency (%)
55.373533177
< 0.1%
54.957850847
< 0.1%
54.559593481
 
< 0.1%
54.547391151
 
< 0.1%
54.533577842
 
< 0.1%
54.52555721
 
< 0.1%
54.520312941
 
< 0.1%
54.515001751
 
< 0.1%
54.511332571
 
< 0.1%
54.501512441
 
< 0.1%

Deaths_rel
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct79424
Distinct (%)50.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05106931741
Minimum0
Maximum0.6329227037
Zeros21751
Zeros (%)13.7%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-06-02T11:15:57.954496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0006251655953
median0.008255269026
Q30.06859123869
95-th percentile0.234716978
Maximum0.6329227037
Range0.6329227037
Interquartile range (IQR)0.06796607309

Descriptive statistics

Standard deviation0.08570775612
Coefficient of variation (CV)1.678263201
Kurtosis7.674926271
Mean0.05106931741
Median Absolute Deviation (MAD)0.008255269026
Skewness2.482942434
Sum8090.605541
Variance0.007345819459
MonotonicityNot monotonic
2022-06-02T11:15:58.142536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
021751
 
13.7%
3.317611541 × 10-5459
 
0.3%
0.0006735049873435
 
0.3%
0.0003107211216343
 
0.2%
0.0007844816969337
 
0.2%
0.0003009939136303
 
0.2%
0.001255339713290
 
0.2%
0.002430361464266
 
0.2%
3.537013395 × 10-5254
 
0.2%
0.0002199092654252
 
0.2%
Other values (79414)133734
84.4%
ValueCountFrequency (%)
021751
13.7%
7.109181074 × 10-82
 
< 0.1%
1.421836215 × 10-74
 
< 0.1%
2.132754322 × 10-72
 
< 0.1%
2.84367243 × 10-72
 
< 0.1%
2.98681082 × 10-72
 
< 0.1%
3.554590537 × 10-71
 
< 0.1%
3.582502278 × 10-72
 
< 0.1%
4.613675412 × 10-77
 
< 0.1%
4.643525962 × 10-71
 
< 0.1%
ValueCountFrequency (%)
0.63292270371
< 0.1%
0.6328959851
< 0.1%
0.63285739121
< 0.1%
0.6328247351
< 0.1%
0.63280395371
< 0.1%
0.63277426621
< 0.1%
0.632741611
< 0.1%
0.6327059851
< 0.1%
0.63268817251
< 0.1%
0.63265848491
< 0.1%

Doses_admin_per_100
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct36968
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.10804371
Minimum0
Maximum327.6076072
Zeros79868
Zeros (%)50.4%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-06-02T11:15:58.322720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q355.50545888
95-th percentile178.2273478
Maximum327.6076072
Range327.6076072
Interquartile range (IQR)55.50545888

Descriptive statistics

Standard deviation61.19020206
Coefficient of variation (CV)1.648974075
Kurtosis1.892203369
Mean37.10804371
Median Absolute Deviation (MAD)0
Skewness1.686068058
Sum5878804.717
Variance3744.240828
MonotonicityNot monotonic
2022-06-02T11:15:58.496735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
079868
50.4%
125.6315512199
 
0.1%
59.24168064119
 
0.1%
244.2055613119
 
0.1%
112.244064596
 
0.1%
5.69723218381
 
0.1%
70.3050079481
 
0.1%
47.8215575881
 
0.1%
54.3338555280
 
0.1%
159.79076379
 
< 0.1%
Other values (36958)77621
49.0%
ValueCountFrequency (%)
079868
50.4%
1.302476145 × 10-51
 
< 0.1%
1.5486728 × 10-51
 
< 0.1%
2.041565039 × 10-51
 
< 0.1%
3.049500188 × 10-51
 
< 0.1%
3.494279728 × 10-51
 
< 0.1%
5.200749077 × 10-51
 
< 0.1%
6.615193406 × 10-520
 
< 0.1%
7.672008737 × 10-51
 
< 0.1%
9.072154473 × 10-51
 
< 0.1%
ValueCountFrequency (%)
327.60760721
< 0.1%
327.28547631
< 0.1%
325.63567322
< 0.1%
324.83479061
< 0.1%
323.97909471
< 0.1%
323.46362691
< 0.1%
323.35849361
< 0.1%
323.06679881
< 0.1%
322.83694921
< 0.1%
322.04967041
< 0.1%

GDP_pro_Kopf
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct178
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14583.95132
Minimum0
Maximum114720.3096
Zeros6027
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-06-02T11:15:58.670092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile389.5279081
Q11915.243745
median5901.937281
Q318150.5165
95-th percentile56828.87032
Maximum114720.3096
Range114720.3096
Interquartile range (IQR)16235.27276

Descriptive statistics

Standard deviation20232.7458
Coefficient of variation (CV)1.387329494
Kurtosis4.515226989
Mean14583.95132
Median Absolute Deviation (MAD)5063.000255
Skewness2.10415816
Sum2310447904
Variance409364002.7
MonotonicityNot monotonic
2022-06-02T11:15:58.848056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06027
 
3.8%
507.4790965861
 
0.5%
2379.853401861
 
0.5%
45923.57646861
 
0.5%
1837.854582861
 
0.5%
407.4581558861
 
0.5%
2288.945873861
 
0.5%
6582.411796861
 
0.5%
79585.11223861
 
0.5%
15807.30854861
 
0.5%
Other values (168)144648
91.3%
ValueCountFrequency (%)
06027
3.8%
240.6504586861
 
0.5%
294.1819461861
 
0.5%
389.5279081861
 
0.5%
407.4581558861
 
0.5%
464.1155755861
 
0.5%
492.4226478861
 
0.5%
495.2559985861
 
0.5%
495.3477182861
 
0.5%
507.4790965861
 
0.5%
ValueCountFrequency (%)
114720.3096861
0.5%
84423.59765861
0.5%
80710.94393861
0.5%
79585.11223861
0.5%
77653.57144861
0.5%
68461.01315861
0.5%
66301.585861
0.5%
65932.8623861
0.5%
62476.28514861
0.5%
56828.87032861
0.5%

Interactions

2022-06-02T11:15:49.860578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:17.767685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:20.526677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:23.203588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:26.197339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:29.298719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:32.407013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:35.493568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:38.393022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:41.251436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:44.192085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:47.120093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:50.099571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:18.017051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:20.743954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:23.430547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:26.478743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:29.537757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:32.674010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:35.729784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:38.625500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:41.518467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:44.429079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:47.357824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:50.326574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:18.235047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:20.952951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:23.643547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:26.762917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:29.753992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:32.938186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:35.963513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:38.853820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:41.775478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:44.662085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:47.589050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:50.562570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:18.465228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:21.152207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:23.852894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:26.998950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:29.988993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:33.175498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:36.195497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:39.072818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:42.030214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:44.878083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:47.809082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:50.793571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:18.703227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:21.375390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:24.080184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:27.266263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:30.229283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:33.437817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:36.448665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:39.318451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:42.311511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:45.115786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:48.045082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:51.041540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:18.940228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:21.685656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:24.316211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:27.518476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:30.488404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:33.689008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:36.680939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:39.569470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:42.553543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:45.348823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:48.278079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:51.285571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:19.162227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:21.906967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:24.568355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:27.771477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:30.766403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:33.937010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:37.044172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:39.815098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:42.797545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:45.586104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:48.514079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:51.507572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:19.384488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:22.113930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:24.823393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:28.008440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:31.028628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:34.208007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:37.258486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:40.042403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:43.029928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:45.797104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:48.727077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:51.742573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:19.606449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:22.338177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:25.095392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:28.259757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:31.321629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:34.519040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:37.482451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:40.283402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:43.275080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:46.038311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:48.961081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:51.975539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:19.841486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:22.546143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:25.378734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:28.593756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:31.585636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:34.778606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:37.719487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:40.513775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:43.511082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:46.263319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:49.171080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:52.219573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:20.068675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:22.758396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:25.651725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:28.827757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:31.860631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:35.017601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:37.934711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:40.760213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:43.732080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:46.484478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:49.410045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:52.455540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:20.298635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:22.980361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:25.932064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:29.063719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:32.150826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:35.245569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:38.163710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:41.003173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:43.969048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:46.730633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-02T11:15:49.634568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-06-02T11:15:59.014054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-02T11:15:59.260051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-02T11:15:59.488337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-02T11:15:59.722510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-02T11:15:52.732914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-02T11:15:53.221915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0DateCountryConfirmed_CasesDeathsNew_CasesNew_DeathsDoses_adminGDPPopulationConfirmed_Cases_relDeaths_relDoses_admin_per_100GDP_pro_Kopf
002020-01-22Afghanistan00000.02.068200e+10407543880.00.00.0507.479096
112020-01-22Albania00000.01.721000e+1028663740.00.00.06004.101349
222020-01-22Algeria00000.01.930560e+11453501480.00.00.04257.009260
332020-01-22Andorra00000.00.000000e+00774630.00.00.00.000000
442020-01-22Angola00000.09.642600e+10350273430.00.00.02752.877945
562020-01-22Antigua and Barbuda00000.01.809000e+09995090.00.00.018179.260167
672020-01-22Argentina00000.05.153530e+11460102340.00.00.011200.834145
782020-01-22Armenia00000.01.386800e+1029719660.00.00.04666.271418
892020-01-22Australia00000.01.481460e+12260687920.00.00.056828.870321
9102020-01-22Austria00000.04.816780e+1190667100.00.00.053125.996089

Last rows

Unnamed: 0DateCountryConfirmed_CasesDeathsNew_CasesNew_DeathsDoses_adminGDPPopulationConfirmed_Cases_relDeaths_relDoses_admin_per_100GDP_pro_Kopf
1584141696052022-05-31United Arab Emirates9082052305381024915902.04.491300e+11100817859.0083750.022863247.13780344548.658794
1584151696062022-05-31United Kingdom224869971793225801110144565659.02.927080e+126849790732.8287360.261792211.05120642732.400568
1584161696072022-05-31Uruguay92577772389389118408518.06.342100e+10349601626.4809140.207036240.51714918140.935282
1584171696082022-05-31Uzbekistan23902816373050434421.05.548200e+10343820840.6952110.004761146.6880861613.689269
1584181696092022-05-31Vanuatu908814450301000.09.940000e+083218322.8238340.00435093.5270583088.567948
1584191696102022-05-31Venezuela523654572136037860994.07.010600e+10292669911.7892310.019548129.3641502395.394867
1584201696112022-05-31Vietnam107193794307910101220779518.02.823720e+119895354110.8327390.043535223.1143182853.581561
1584211696142022-05-31Yemen11822214900836455.03.138500e+10311548670.0379460.0068982.6848291007.386743
1584221696152022-05-31Zambia321779398727623478542.02.527200e+10194702341.6526710.02047717.8659491297.981319
1584231696162022-05-31Zimbabwe2523985503306311742863.02.581200e+10153314281.6462780.03589476.5934071683.600510